AIMC Topic: Mathematics

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Learning Spatial-Spectral-Temporal EEG Features With Recurrent 3D Convolutional Neural Networks for Cross-Task Mental Workload Assessment.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Mental workload assessment is essential for maintaining human health and preventing accidents. Most research on this issue is limited to a single task. However, cross-task assessment is indispensable for extending a pre-trained model to new workload ...

Significant predictors of mathematical literacy for top-tiered countries/economies, Canada, and the United States on PISA 2012: Case for the sparse regression model.

The British journal of educational psychology
BACKGROUND: National ranking from the triennial Programme of International Student Assessment (PISA) often serves as a barometer of national performance and human capital. Though excessive student- and school-level covariates (n > 700) may prove intr...

Inferring locomotor behaviours in Miocene New World monkeys using finite element analysis, geometric morphometrics and machine-learning classification techniques applied to talar morphology.

Journal of the Royal Society, Interface
The talus is one of the most commonly preserved post-cranial elements in the platyrrhine fossil record. Talar morphology can provide information about postural adaptations because it is the anatomical structure responsible for transmitting body mass ...

Are computational applications the "crystal ball" in the IVF laboratory? The evolution from mathematics to artificial intelligence.

Journal of assisted reproduction and genetics
Mathematics rules the world of science. Innovative technologies based on mathematics have paved the way for implementation of novel strategies in assisted reproduction. Ascertaining efficient embryo selection in order to secure optimal pregnancy rate...

AVNM: A Voting based Novel Mathematical Rule for Image Classification.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: In machine learning, the accuracy of the system depends upon classification result. Classification accuracy plays an imperative role in various domains. Non-parametric classifier like K-Nearest Neighbor (KNN) is the most wi...

Performing mathematics activities with non-standard units of measurement using robots controlled via speech-generating devices: three case studies.

Disability and rehabilitation. Assistive technology
Purpose To examine how using a Lego robot controlled via a speech-generating device (SGD) can contribute to how students with physical and communication impairments perform hands-on and communicative mathematics measurement activities. This study was...

A non-penalty recurrent neural network for solving a class of constrained optimization problems.

Neural networks : the official journal of the International Neural Network Society
In this paper, we explain a methodology to analyze convergence of some differential inclusion-based neural networks for solving nonsmooth optimization problems. For a general differential inclusion, we show that if its right hand-side set valued map ...

Constructing general partial differential equations using polynomial and neural networks.

Neural networks : the official journal of the International Neural Network Society
Sum fraction terms can approximate multi-variable functions on the basis of discrete observations, replacing a partial differential equation definition with polynomial elementary data relation descriptions. Artificial neural networks commonly transfo...

Generative AI without guardrails can harm learning: Evidence from high school mathematics.

Proceedings of the National Academy of Sciences of the United States of America
Generative AI is poised to revolutionize how humans work, and has already demonstrated promise in significantly improving human productivity. A key question is how generative AI affects learning-namely, how humans acquire new skills as they perform t...

Navigating Mathematical Basics: A Primer for Deep Learning in Science.

Advances in experimental medicine and biology
We present a gentle introduction to elementary mathematical notation with the focus of communicating deep learning principles. This is a "math crash course" aimed at quickly enabling scientists with understanding of the building blocks used in many e...